Multi-energy park scheduling method and system based on double-layer reinforcement learning

Abstract

The invention provides a multi-energy park scheduling method and system based on double-layer reinforcement learning. The method comprises the steps of: obtaining scheduling controllable objects, i.e., a source side unit, a load side unit, an energy conversion unit and a storage unit, in an integrated energy system, constructing a double-layer optimization decision model which comprises an upper-layer reinforcement learning sub-model and a lower-layer mixed integer linear programming sub-model, enabling the upper-layer reinforcement learning sub-model to acquire action variable information ofthe storage unit under the state variable information at the current moment and transmit the action variable information to the lower-layer mixed integer linear programming sub-model, enabling the lower-layer mixed integer linear programming sub-model to acquire a corresponding award variable and state variable information of the storage unit at the next moment, and feed back the award variable and the state variable information to the upper-layer reinforcement learning sub-model, and iteratively executing the above steps until the scheduling is finished. According to the embodiment of the invention, through a data-driven reinforcement learning method, a decision only needs to be made according to the current state, future information does not need to be predicted, the decision timelinessis high, the decision effect is excellent, and a real-time optimization decision can be realized.

Description

technical field [0001] The embodiment of the present invention relates to the field of computer technology, in particular to a multi-energy park scheduling method and system based on double-layer reinforcement learning. Background technique [0002] In recent years, with the crisis of fossil energy and the increasingly prominent environmental problems, countries all over the world are looking for new energy utilization methods. The development trend of future energy has the following characteristics: the demand for energy continues to grow, and fossil energy will still be the main primary energy for a long time in the future; the urgency of environmental issues requires constant adjustment of the energy structure with environmental issues as the core; The proportion of renewable energy continues to increase. [0003] Under various energy pressures, the construction of an integrated energy system can achieve multi-energy coupling complementarity and cascaded utilization, so ...

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Problems solved by technology

[0008] Finally, the introduction of renewable energy will bring great uncertainty to the comprehensive energy system, and when the scale of the multi-energy park is small, the effect of load due to the randomness of users is more obvious

Therefore, when the future information of the system load, such as: renewable energy output and system load, is difficult to obtain or accurately predict, traditional optimization and dynamic programming methods are difficult to solve the difficult scheduling problem of the multi-energy park energy system

[0009] To sum up, on the one hand, the existing technology is difficult to deal with strong uncertainty scenarios, and it is difficult to deal with scenarios where future information is difficult to predict, and when performing optimization solutions, the solution speed is relatively slow

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